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The purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).

The specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.

The authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.

The proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.

This study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

The underlying managerial rationale for segmenting markets is well established, with the marketing literature citing a range of benefits for businesses adopting a segmentation approach. Yet organisations frequently encounter difficulties in implementing segmentation principles. Even in the industrial marketing literature, where the most practical implementation guidance is offered, it is suggested that organisations tend to over‐emphasise the mechanics of segmentation, while failing to correctly implement the findings. This suggests that, if organisations are to benefit from applying segmentation principles, two fundamental questions should be addressed. The first concerns the basic conditions which must be met if implementation is to be effected. That is, it must be possible to map the dimensions developed on to usable customer characteristics. The second concerns the costs and benefits of the segmentation solution in relation to the proportion of variance in customer requirements which it explains. Addresses these questions in an industrial marketing context, using a literature review and quantitative analysis of data from the European car parts after market. The analysis shows that whilst traditional segmentation methods can be used to identify certain segments, these segments do not readily map on to implementable dimensions. Furthermore, it is suggested that even a relatively wide use of structural independent variables explains only a very small proportion of the individual variability in customer requirements. This suggests that much segmentation analysis may be poorly directed and also of rather limited practical value.

This paper discusses the dos and don'ts of market segmentation analysis. Market segmentation analysis is younger than the journal Tourism Review, but nevertheless has a rich history in tourism research and continues to be extensively used by both tourism researchers and industry.

After a brief overview of the origins of market segmentation analysis and its uptake in tourism, a number of key considerations are discussed, which are critical to ensuring that practically useful and reliable market segments emerge from the analysis.

Do accept that market segmentation is exploratory. Do spend a lot of time ensuring you collect high-quality data. Don’t use ordinal data. Don’t use correlated variables. Do ensure your sample size is large enough. Don’t use factor-cluster analysis. Do conduct data structure analysis. Don’t complicate things.

This is a perspective study; it offers a concise discussion of key issues in market segmentation analysis and directs the interested reader to resources where they can learn more about each of these issues.

It is important to remain creative when conducting segmentation research, as many different ways to segment a market can exist. Five main bases are discussed: geographic, demographic, psychographic, behaviouristic and image. This is followed by an overview of the main techniques used to establish and verify segments, including automatic interaction detector, conjoint analysis, multidimensional scaling and canonical analysis.

Organizations wishing to apply the principles of market segmentation often face problems putting the theory into practice. All too often the required background analysis is inadequate or poorly structured or the translation of segmentation strategy into marketing programs is impeded. To be successful, segmentation must lead an organization through a process which undertakes background analysis, determines strategy and develops marketing programs. However, there are a number of points at which the process can break down. Shows how the segmentation program described has tackled these difficulties, leading several management teams through the analysis, strategy and program elements of the market segmentation process. A range of benefits arise from the program. Primary benefits are that the process puts the customer first, maximizes resources and emphasizes strengths over competitors. Secondary benefits relate to the development of a more market‐focussed company culture and the building of inter‐ and intra‐organizational relationships.

Demonstrates the usefulness of combining retail market segmentation with competitive analysis as a very effective method to understand the dynamics of retail markets and to analyse strategic options for supermarket chains. Attempts to advance the market segmentation research by narrowing the gap between the academically oriented research on segmentation and the practical application of segmentation research. Applies the recommended methodology to a largescale investigation and discusses the empirical findings along with strategic implications for supermarket retail chain organizations.

This study aims to capture the views of executives about the merit of using the two segmentation approaches (quantitative vs qualitative). Furthermore, this study aimed to examine costs and benefits for two different segmentation approaches, using a minimax simple cost-benefit analysis (CBA) matrix.

A total of 16 semistructured interviews were conducted with executives within the University of Sharjah (UoS). Furthermore, a minimax approach was applied to the CBA study.

Evidence in this study found that the financial cost of quantitative segmentation approaches was higher than qualitative approaches. However, the decision-makers trusted the quantitative approach more regardless of the incurred costs. The study also found that there was a limited knowledge about social marketing and segmentation among executives.

Limitations of this study relate to the methodology applied, the sample selected and the lead research. Another factor is selection bias, which limited this study to one organization’s executives. It is conceivable that middle-level management would have had the desire to participate because they make the recommendations to top management in decision-making. The researcher did not collect precise data on time taken to design, implement and analyses the two segmentation studies, which qualified the precision of the CBA. Also, the fact that the sample includes participants from a relatively narrow range of disciplines should be noted as a limitation of the study.

The current study provides a case study demonstrating how CBA provides a dollar amount estimate permitting alternate segmentation approaches to be compared and contrasted, assisting in the value estimation of any social marketing project.

The paper draws upon two streams of the literature: social marketing and CBA. The paper focused on the understanding of the literature, CBA offers a technique applicable to demonstrating cost savings that can be derived from choosing one method over another. Moreover, CBA assists in understanding the benefits or potential opportunity cost both financially and nonfinancially.

This paper presents one of the first studies conducting a CBA to compare and contrast two segmentation approaches in social marketing. The study provides interesting insights into the perceptions of management executives over alternative research methods, although the results are limited to a case study.

Customer service represents a significant opportunity for segmenting markets. This article reviews the importance of customer service and the conceptual issues associated with segmenting industrial markets on the basis of customer service. A methodology is presented which can be used by managers to classify a market into segments with different customer service needs. Empirical results from a high‐technology industry are also presented. The article emphasises the need to recognise the differing customer service requirements of segments of customers when establishing priorities for customer service expenditures.

The purpose of this paper is to explore the possibility of identifying market segments in multistage markets and assessing whether their alignment could provide a useful managerial approach to find competitive advantage and better understand market opportunities.

Using data from a pilot project, need-based market segments from different market stages were identified and their potential alignment evaluated. The data were not designed to test hypotheses, nor were they originally intended to be used to align segments. Nevertheless, they provided a unique opportunity to explore multistage segmentation and segment alignment in a business-to-business (B2B) setting.

Overall, the findings of this exploratory study should encourage both academics and practitioners to continue to explore the possibility of studying and aligning multistage market segments. The possibility of aligning segments was demonstrated using visual alignment based on managerial judgment of data and alignment based on a combined cluster analysis of customers across the multistage markets.

First, the market research was not specifically designed to formulate and test hypotheses about the feasibility of aligning segments in multistage markets – it is an exploratory study. The research was based on a pilot project, and the survey-derived databases were conveniently available for analysis. While sample sizes were small, they are typical of many B2B markets. Second, to more effectively study complex relationships in multistage markets, it would have been desirable to include a more comprehensive set of needs. Each market stage has not only a set of their own perceived needs but also a set of perceptions of the needs of other stages. Third, as in many B2B studies, the data used in this pilot project were based on single informants.

A common complaint among firms is that B2B market segmentation does not really work that well for them. An unexplored reason for this may be that true market segmentation does not stop with one’s direct customer, but should also include the customer’s customer and so on, in a multistage market segmentation structure. One implication of the research presented here suggests that better understanding the segmentation structure in a multistage market can enlighten the opportunities and risks of implementing such a strategy. Multistage market segmentation alignment may lead to innovative positioning and message levers for the sales force to use as an argument to gain advantage according to common and unique aligned segment needs.

The process may be applied to social institutions in addition to commercial organizations.

While it is obvious that market segmentation can be applied to any single market of customers, the question of applying it to complex multistage markets needs additional exploration. The original idea in this paper is that the potential for strategically aligning multistage markets and segments can have both conceptual and managerial implications for establishing competitive advantage and more efficient and effective resource allocation. The paper shows that that such alignment is possible; however, research and research methods in this area are nascent and will require continued step-by-step learning about these complex market structures to build up to a more definitive understanding of the processes involved to guide future research and managerial thinking.

Data-driven market segmentation is heavily used by academic tourism and hospitality researchers to create knowledge and by data analysts in tourism industry to generate market insights. The stability of market segmentation solutions across repeated calculations is a key quality indicator of a segmentation solution. Yet, stability is typically ignored, risking that the segmentation solution arrived at is random. This study aims to offer an overview of market segmentation analysis and propose a new procedure to increase the stability of market segmentation solutions derived from binary data.

The authors propose a new method – based on two independently proposed algorithms – to increase the stability of market segmentation solutions. They demonstrate the superior performance of the new method using empirical data.

The proposed approach uses k-means as base algorithm and combines the variable selection method proposed by Brusco (2004) with the global stability analysis introduced by Dolnicar and Leisch (2010). This new approach increases the stability of segmentation solutions by simultaneously selecting variables and numbers of segments.

The new approach can be adopted immediately by academic researchers and industry data analysts alike to improve the quality of market segmentation solutions derived from empirical tourist data. Higher quality market segmentation solutions translate into competitive advantage and increased business or destination performance.

The proposed approach is newly developed in this study. It helps industry data analysts and academic researchers to reduce the risk of deriving random segmentation solutions by analyzing the data in a systematic way, then selecting the most stable solution using the segmentation variables contributing to this most stable solution only.